Marketing programs can include, for example, mail and direct mail campaigns, inbound and outbound telemarketing campaigns, and inbound and outbound web and e-mail campaigns.
In the field of customer targeting, a single segmentation method is used to attempt to select those customers who are most likely to respond to marketing programs. This initial segmentation is sometimes, but rarely, followed by a second independent segmentation to determine which customers are most likely to spend more if they respond. This second segmentation of customers is used primarily to reduce quantities of targeted customers contacted in order to meet a pre-specified criteria.
Segmentation strategies well known to one in the art can include, for example, linear models, logistic models, RFM segmentation, and CHAID (Chi-square Automatic Interaction Detection) segmentation.
Conventional wisdom in the marketing industry recognizes that each of the segmentation strategies perform better than the other strategies in certain situations depending on a number of various circumstances. Accordingly, through past experience, or by running different trials, the segmentation strategy is typically selected which optimizes, or emphasizes, the differentiation between the customer population for a desired outcome. The other, unselected segmentation strategies are then ignored because they are considered to be weaker indicators of the variance within the customer population. Within the framework of the selected segmentation strategy, additional improvements to the results can be attempted but usually only by adding additional variables to be considered during the model analysis.
In the 1930s, RFM segmentation was developed. This method of segmentation sorts customers by the Recency of their last purchase, then by the Frequency of their purchases recorded on file, and finally by the Monetary value of their purchases recorded on file. RFM segmentation, and variations thereof, are still the primary methods used today by marketers to segment customers.
With the advent of computer technology and automation, companies have begun to shift to regression based segmentation methods. These relatively new methods for segmentation involve creating variables based on customers' purchases and demographic data. Next a specific event is targeted, or identified, (e.g., the likelihood of purchasing from a particular catalog) and then, the regression is run to create a statistical model that attempts to predict the targeted event. Almost always, regression methods of segmentation provide better results than RFM segmentation.
But, if RFM segmentation produces, for example, response rates of 3%, and running a regression produces response rates of 4%, then there is still room for improving response rates to the remaining 96% of customers and potential customers that have had marketing material remitted to them. These contacted but non-responding groups represent the bulk of the expenses involved in marketing today.